Sklearn DBSCAN icon

Sklearn DBSCAN

DBSCAN clustering algorithm using scikit-learn

Sklearn DBSCAN

AI-generated

Summary

Cluster input data using DBSCAN and attach clustering metadata

Inputs

  • featureColumns (required) — Comma-separated list of column names to use as features; values are parsed as floats
  • eps — Maximum distance between samples to be considered neighbors
  • minSamples — Minimum samples in a neighborhood to form a core point
  • metric — Distance metric to use (euclidean, manhattan, cosine, chebyshev)
  • pythonPath — Path to Python executable with scikit-learn installed

Output shape

an array of records

Each record contains the original input data plus cluster label, is_noise flag, is_core_sample flag, n_clusters, and n_noise values

Examples

Example 1: Cluster a dataset with two feature columns

featureColumns: 'col1,col2', eps: 0.4, minSamples: 4, metric: 'euclidean'

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